Design and Analysis Issues for Economic Analysis Alongside Clinical Trials
Bibliographic record
Abstract
INTRODUCTION: Clinical trials can offer a valuable and efficient opportunity to collect the health resource use and outcomes data for economic evaluation. However, economic and clinical studies differ fundamentally in the question they seek to answer. OBJECTIVE: The design and analysis of trial-based cost-effectiveness studies require special consideration, which are reviewed in this article. SUMMARY: Traditional randomized controlled trials, using an experimental design with a controlled protocol, are designed to measure safety and efficacy for product registration. Cost-effectiveness analysis seeks to measure effectiveness in the context of routine clinical practice, and requires collection of health care resources to allow estimation of cost over an equal timeframe for each treatment alternative. In assessing suitability of a trial for economic data collection, the comparator treatment and other protocol factors need to reflect current clinical practice and the trial follow-up must be sufficiently long to capture important costs and effects. The broadest available population and a measure of effectiveness reflecting important benefits for patients are preferred for economic analyses. Special analytical issues include dealing with missing and censored cost data, assessing uncertainty of the incremental cost-effectiveness ratio, and accounting for the underlying heterogeneity in patient subgroups. Careful consideration also needs to be given to data from multinational studies since practice patterns can differ across countries. CONCLUSION: Although clinical trials can be an efficient opportunity to collect data for economic evaluation, careful consideration of the suitability of the study design, and appropriate analytical methods must be applied to obtain rigorous results.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.564 | 0.751 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.015 | 0.020 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".